Abstract:

Beginning with the two dimensional topological structure of molecular, we represent 338 equivalence resonance carbons just by atomic elementary electronegativity interaction vectors (AEIV) of the molecules, we and build multiple linear regression (MLR) modeling for calculating ¹³C chemical shifts in furanose using the chemical shifts of 148 ¹³C atoms in 25 furanose derivatives as the training set. The correlation coefficient R_cum of the model was found to be 0.907. Leave-one-out cross-validation was used to verify the predictive abilities of the model, resulting a correlation coefficient Q_cum of 0.901. 

Key words: ¹³C NMR chemical shift, furanose, atomic elementary electronegativity interaction vector, multiple linear regression